1. Field of the Invention
The present invention relates to an image processing apparatus, an image processing method and a program for extracting a local feature.
2. Description of the Related Art
As described in “C. Schmid and R. Mohr, ‘Local gray value invariants for image retrieval’ IEEE Trans. PAMI. Vol. 19, No. 5, pp 530-535, 1997”, there is a method for searching for a similar image using a local feature quantity acquired by quantifying a local feature of an image. According to this method, firstly, a differential filter such as Sobel and Prewitte is applied to a two-dimensional brightness distribution of the image to extract a feature point included in an edge portion or a corner portion of the image. Next, the feature quantity of the feature point (local feature quantity) is calculated from pixel values of the feature point and pixels located in the vicinity thereof. The image is searched for by matching the local feature quantities with each other.
However, some extracted feature points are not stable enough that they cannot be extracted from the same edge portion or the same corner portion again (low reproducibility) after the image is a little rotated, enlarged or reduced. Such an extracted feature point having the low reproducibility often works as a noise and can deteriorate a search accuracy or a positioning accuracy. Therefore, Japanese Patent Application Laid-Open No. 9-44665 and Scmid discuss a method for providing the threshold for a function value used in extracting the feature point and the pixel value of the feature point candidate and discarding the feature point candidate that shows less than the threshold value.
It is not sufficient to use only the predetermined pixel value and the function value used in extracting the feature point to remove the unstable feature point. Thus, it is necessary to set the threshold value high enough in order to narrow down to the feature point having a high reproducibility, which can be extracted even if the image is variously converted. However, if the threshold value is set too high, only a few feature points are acquired, which can greatly deteriorate the search accuracy and a recognition accuracy.